Triplet network for classification of benign and pre-malignant polyps

Roger Fonollà, Maciej Smyl, Fons Van Der Sommen, Ramon M. Schreuder, Erik J. Schoon, Peter H.N. De With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

Colorectal polyps are critical indicators of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models, albeit with limited success. An early detection of CRC prevents further complications in the colon, which makes identification of abnormal tissue a crucial step during routinary colonoscopy. In this paper, a classification approach is proposed to differentiate between benign and pre-malignant polyps using features learned from a Triplet Network architecture. The study includes a total of 154 patients, with 203 different polyps. For each polyp an image is acquired with White Light (WL), and additionally with two recent endoscopic modalities:Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). The network is trained with the associated triplet loss, allowing the learning of non-linear features, which prove to be a highly discriminative embedding, leading to excellent results with simple linear classifiers. Additionally, the acquisition of multiple polyps with WL, BLI and LCI, enables the combination of the posterior probabilities, yielding a more robust classification result. Threefold cross-validation is employed as validation method and accuracy, sensitivity, specificity and area under the curve (AUC) are computed as evaluation metrics. While our approach achieves a similar classification performance compared to state-of-the-art methods, it has a much lower inference time (from hours to seconds, on a single GPU). The increased robustness and much faster execution facilitates future advances towards patient safety and may avoid time-consuming and costly histhological assessment.

Original languageEnglish
Title of host publicationMedical Imaging 2021
Subtitle of host publicationComputer-Aided Diagnosis
EditorsMaciej A. Mazurowski, Karen Drukker
PublisherSPIE
ISBN (Electronic)9781510640238
DOIs
Publication statusPublished - 2021
EventMedical Imaging 2021: Computer-Aided Diagnosis - Virtual, Online, United States
Duration: 15 Feb 202119 Feb 2021

Publication series

NameProceedings of SPIE
Volume11597
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Computer-Aided Diagnosis
CountryUnited States
CityVirtual, Online
Period15/02/2119/02/21

Bibliographical note

Publisher Copyright:
© COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Blue Laser Imaging
  • Linear SVM
  • Linked Color Imaging
  • Polyp classification
  • Triplet Network

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